Why do “Neural Signal Processing”?
The activity of a complex network of billions of interconnected neurons underlies our ability to sense, represent and store the details of experienced life, and enables us to interact with our environment and other organisms. Modern neuroscience techniques enable us to access this activity, and thus to begin to understand the processes whereby individual neurons work together to enable complex behaviors. In order to increase this understanding and to design biomedical systems which might therapeutically interact with neural circuits, advanced statistical signal processing and machine learning approaches are required.
What does ELEC548/483 cover?
This class will cover a range of techniques and their application to basic neuroscience and neural interfaces. Topics include an introduction to neurobiology and electrophysiology for engineers, point processes, dimensionality reduction, classification/clustering, spectral analysis, and genetic and optical tools for interrogating neural circuits. Neuroscience applications include modeling action potentials and firing rates, automated analysis of activity-dependent fluorescence imaging data, decoding, spike sorting, and field potential analysis. This course is open to students with no prior neurobiology coursework.
Instructor – Caleb Kemere (caleb.kemere@rice.edu)
Location – BRC284
Time – Tuesdays/Thursdays 10:50 AM - 12:05 PM
Canvas – https://canvas.rice.edu/courses/41372
Syllabus – here
Lectures: link to dropbox folder
Course Notes
- Byrons Review of Point Processes
- Notes on Classification
- Notes on Mixture Models
- Notes on Dimensionality Reduction
- Notes on the Kalman Filter
Assignments:
Useful References / Final Project Papers
Online Resources
- Interactive Membrane Potential Simulator
- Interactive Simulator for the Hodgkin Huxley Model Neuron
- Computational Cognition Cheat Sheets
Previous versions of the class